{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot review data in pandas\n",
    "\n",
    "This notebook shows how data can be pivoted by python [pandas](https://pandas.pydata.org/) to reveal insights into the behaviour of reviewers. The use case and data is from Mark Harwood's talk on [entity-centric indexing](https://www.elastic.co/videos/entity-centric-indexing-mark-harwood).\n",
    "\n",
    "An alternative version of this notebook uses the [Elastic data frames](https://www.elastic.co/guide/en/elastic-stack-overview/master/ml-dataframes.html) to create the same results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bz2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data to pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_handle = bz2.open('./anonreviews.csv.bz2')\n",
    "\n",
    "reviews = pd.read_csv(csv_handle)\n",
    "\n",
    "reviews['date'] = pd.to_datetime(reviews['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewerId</th>\n",
       "      <th>vendorId</th>\n",
       "      <th>rating</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006-04-07 17:08:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2006-05-04 12:16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2006-04-21 12:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2006-04-18 15:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2006-04-18 15:49:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reviewerId  vendorId  rating                date\n",
       "0           0         0       5 2006-04-07 17:08:00\n",
       "1           1         1       5 2006-05-04 12:16:00\n",
       "2           2         2       4 2006-04-21 12:26:00\n",
       "3           3         3       5 2006-04-18 15:48:00\n",
       "4           3         4       5 2006-04-18 15:49:00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewerId</th>\n",
       "      <th>vendorId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>578805.000000</td>\n",
       "      <td>578805.000000</td>\n",
       "      <td>578805.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>174124.098437</td>\n",
       "      <td>60.645267</td>\n",
       "      <td>4.679671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>116951.972209</td>\n",
       "      <td>54.488053</td>\n",
       "      <td>0.800891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>70043.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>161052.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>272697.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>400140.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          reviewerId       vendorId         rating\n",
       "count  578805.000000  578805.000000  578805.000000\n",
       "mean   174124.098437      60.645267       4.679671\n",
       "std    116951.972209      54.488053       0.800891\n",
       "min         0.000000       0.000000       0.000000\n",
       "25%     70043.000000      20.000000       5.000000\n",
       "50%    161052.000000      44.000000       5.000000\n",
       "75%    272697.000000      83.000000       5.000000\n",
       "max    400140.000000     246.000000       5.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 578805 entries, 0 to 578804\n",
      "Data columns (total 4 columns):\n",
      "reviewerId    578805 non-null int64\n",
      "vendorId      578805 non-null int64\n",
      "rating        578805 non-null int64\n",
      "date          578805 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(3)\n",
      "memory usage: 17.7 MB\n"
     ]
    }
   ],
   "source": [
    "reviews.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of reviews (high number of five star ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reviews.hist(column=\"rating\", bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Typically how many vendors does a reviewer review? (mainly one or two)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(reviews.groupby('reviewerId')['vendorId'].nunique(), '.')\n",
    "plt.xlabel('reviewerId')\n",
    "plt.ylabel('dc(vendorId)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate and Pivot data\n",
    "\n",
    "Pivot data so we get summaries for each reviewer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregations = {\n",
    "    'rating':'mean',\n",
    "    'vendorId':'nunique',\n",
    "    'reviewerId':'count'\n",
    "}\n",
    "\n",
    "grouped = reviews.groupby('reviewerId').agg(aggregations)\n",
    "grouped.columns=['avg_rating', 'dc_vendorId', 'count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>dc_vendorId</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reviewerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.571429</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_rating  dc_vendorId  count\n",
       "reviewerId                                \n",
       "0             5.000000            1      1\n",
       "1             5.000000            7      9\n",
       "2             4.000000            5      5\n",
       "3             4.571429            7      7\n",
       "4             5.000000            1      1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>dc_vendorId</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400141.000000</td>\n",
       "      <td>400141.000000</td>\n",
       "      <td>400141.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.643846</td>\n",
       "      <td>1.357017</td>\n",
       "      <td>1.446503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.813586</td>\n",
       "      <td>0.989413</td>\n",
       "      <td>1.541802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_rating    dc_vendorId          count\n",
       "count  400141.000000  400141.000000  400141.000000\n",
       "mean        4.643846       1.357017       1.446503\n",
       "std         0.813586       0.989413       1.541802\n",
       "min         0.000000       1.000000       1.000000\n",
       "25%         5.000000       1.000000       1.000000\n",
       "50%         5.000000       1.000000       1.000000\n",
       "75%         5.000000       1.000000       1.000000\n",
       "max         5.000000      32.000000     168.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,10)\n",
    "\n",
    "scatter_matrix(grouped)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find 'haters'\n",
    "\n",
    "Reviewers that give more than five zero star reviews to one vendor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>dc_vendorId</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reviewerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10392</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17033</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21046</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11479</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27448</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8185</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17602</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13984</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228129</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25267</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53432</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19813</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11987</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135506</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_rating  dc_vendorId  count\n",
       "reviewerId                                \n",
       "10392              0.0            1     94\n",
       "17033              0.0            1     51\n",
       "21046              0.0            1     25\n",
       "11479              0.0            1     20\n",
       "27448              0.0            1     19\n",
       "8185               0.0            1     15\n",
       "17602              0.0            1     15\n",
       "13984              0.0            1     10\n",
       "228129             0.0            1      9\n",
       "25267              0.0            1      8\n",
       "53432              0.0            1      8\n",
       "19813              0.0            1      7\n",
       "11987              0.0            1      6\n",
       "135506             0.0            1      6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped[ \n",
    "    (grouped['dc_vendorId'] == 1) & \n",
    "    (grouped['count'] > 5) & \n",
    "    (grouped['avg_rating'] == 0)\n",
    "].sort_values('count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, reviewer 10392 gives 94 zero star reviews to vendor 122"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewerId</th>\n",
       "      <th>vendorId</th>\n",
       "      <th>rating</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12340</th>\n",
       "      <td>10392</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>2006-04-05 15:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12341</th>\n",
       "      <td>10392</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>2006-04-06 09:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12342</th>\n",
       "      <td>10392</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>2006-04-06 20:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12343</th>\n",
       "      <td>10392</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>2006-04-11 07:43:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12344</th>\n",
       "      <td>10392</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>2006-04-11 15:53:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reviewerId  vendorId  rating                date\n",
       "12340       10392       122       0 2006-04-05 15:38:00\n",
       "12341       10392       122       0 2006-04-06 09:24:00\n",
       "12342       10392       122       0 2006-04-06 20:24:00\n",
       "12343       10392       122       0 2006-04-11 07:43:00\n",
       "12344       10392       122       0 2006-04-11 15:53:00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[reviews['reviewerId'] == 10392].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find 'fanboys'\n",
    "\n",
    "Reviewers that give more than five five star reviews to one vendor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>avg_rating</th>\n",
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       "      <td>5.0</td>\n",
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       "      <td>73</td>\n",
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       "      <th>260225</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>264635</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>321206</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>12539</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>159035</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>114661</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>39655</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>22515</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>180082</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>58447</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
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       "      <th>180085</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <td>5.0</td>\n",
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      "text/plain": [
       "            avg_rating  dc_vendorId  count\n",
       "reviewerId                                \n",
       "183751             5.0            1     73\n",
       "260225             5.0            1     69\n",
       "205864             5.0            1     35\n",
       "345080             5.0            1     23\n",
       "179944             5.0            1     22\n",
       "345082             5.0            1     21\n",
       "345081             5.0            1     20\n",
       "345068             5.0            1     20\n",
       "345069             5.0            1     19\n",
       "345086             5.0            1     18\n",
       "345083             5.0            1     18\n",
       "345070             5.0            1     18\n",
       "345085             5.0            1     17\n",
       "345084             5.0            1     17\n",
       "264635             5.0            1     13\n",
       "321206             5.0            1     12\n",
       "12539              5.0            1     11\n",
       "159035             5.0            1     10\n",
       "114661             5.0            1      9\n",
       "39655              5.0            1      8\n",
       "22515              5.0            1      7\n",
       "180082             5.0            1      7\n",
       "58447              5.0            1      7\n",
       "180085             5.0            1      6\n",
       "168143             5.0            1      6\n",
       "160018             5.0            1      6\n",
       "75010              5.0            1      6\n",
       "35048              5.0            1      6\n",
       "30474              5.0            1      6\n",
       "28814              5.0            1      6\n",
       "393237             5.0            1      6"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "grouped[ \n",
    "    (grouped['dc_vendorId'] == 1) & \n",
    "    (grouped['count'] > 5) & \n",
    "    (grouped['avg_rating'] == 5) \n",
    "].sort_values('count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reviewer 183751 gives 73 five star reviews to vendor 190"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2006-09-22 16:36:00</td>\n",
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      "text/plain": [
       "        reviewerId  vendorId  rating                date\n",
       "261469      183751       190       5 2006-09-22 16:36:00\n",
       "261470      183751       190       5 2006-09-22 16:36:00\n",
       "261471      183751       190       5 2006-09-22 16:35:00\n",
       "261474      183751       190       5 2006-09-22 15:53:00\n",
       "261475      183751       190       5 2006-09-22 15:53:00"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[reviews['reviewerId'] == 183751].head()"
   ]
  }
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